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“All those in favour will please say yea”: Understanding the Factors Behind Machine Translation Adoption at the Canadian Parliament

Summary

Canadian parliamentary translators can choose whether to post-edit output from Hawkeye, an in-house NMT system built on Amazon's Sockeye toolkit, trained on parliamentary data, and embedded in the Prism workflow application. Because MT use is optional rather than mandated, the setting is a rare chance to study why professionals take up MT or avoid it. The NRC team used a mixed-methods explanatory sequential design. A bilingual online questionnaire, which included the I-PANAS-SF affect scale and the SUS usability scale, was followed by semi-structured interviews. Interview questions were shaped by the questionnaire results. Neither professional experience nor PE/MT training predicted adoption. Instead, the main drivers were personal preference, such as liking to translate from scratch or preferring tools like DeepL, along with external constraints. Users valued Hawkeye as a source of ideas and ready-made solutions, and because it reduces typing. The authors call for user-centred MT deployment, with better onboarding material, error-aware training, quality improvements and interface features.

Key points

  • Log data from May 2023 to December 2025 show that the share of House of Commons texts with MT requests grew from 16% to about 58% by December 2024, then stayed level through 2025. Texts post-edited from Hawkeye output needed less revision than texts translated without it.
  • The study drew on a pool of 66 Translation Bureau translators. The questionnaire got 39 responses (a 59% rate). Four were discarded because respondents had confused Hawkeye with an older MT system, leaving n=35. Interviews lasted up to 45 minutes on Microsoft Teams, in French or English.
  • Most non-adopters reported a general dislike of MT, not complaints about Hawkeye specifically. Other reasons were:
    • the system is unavailable for Senate work;
    • it occasionally freezes or crashes;
    • MT does not apply to revision tasks.
  • Reported error types include idioms, figurative language, long sentences, proper names, hallucinations, co-reference and terminology. The authors see co-reference as possibly fixable through wider context windows or external information sources.
  • Recommendations:
    • short guides, cheat sheets or video tutorials instead of full training programs;
    • training on MT errors and PE;
    • UI features such as pre-population, deletion buttons and DeepL-style alternative translations.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages · Machine translation

Topics: Machine translation

Cite

The paper itself, not this page. BibTeX from the library's record of the work.

@inproceedings{lealwyss2026all,
  title = {{“All those in favour will please say yea”: Understanding the Factors Behind Machine Translation Adoption at the Canadian Parliament}},
  author = {Leal-Wyss, Jeniffer and Lothian, Delaney and Bernier-Colborne, Gabriel and Knowles, Rebecca and Simard, Michel},
  booktitle = {Proceedings of the 26th Annual Conference of the European Association for Machine Translation (Volume 1)},
  year = {2026},
  url = {https://aclanthology.org/2026.eamt-1.36/}
}

References

28 references, 1 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Brooke, John. 1996. SUS–a quick and dirty usability scale. In Usability evaluation in industry, pages 189–194. Taylor & Francis. find
  2. Cadwell, Patrick, Sharon O’Brien, and Carlos Teixeira. 2018. Resistance and accommodation: factors for the (non-) adoption of machine translation among professional translators. Perspectives, 26(3):301– 321. find
  3. Creswell, John W and J David Creswell. 2017. Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications. find
  4. Davis, Fred. 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3):319–340. find
  5. Flanagan, Marian, Helle Jensen, Kristine Bundgaard, and Tina Christensen. 2025. Technology adoption among Danish translators: Practices, perceptions and prospects. Tradumàtica tecnologies de la traducció, pages 111–136, 12. find
  6. Gaspari, Federico, Antonio Toral, Sudip Kumar Naskar, Declan Groves, and Andy Way. 2014. Perception vs. reality: measuring machine translation post-editing productivity. In O’Brien, Sharon, Michel Simard, and Lucia Specia, editors, Proceedings of the 11th Conference of the Association for Machine Tr… find
  7. Groves, Declan and Dag Schmidtke. 2009. Identification and analysis of post-editing patterns for MT. In Proceedings of Machine Translation Summit XII: Commercial MT User Program, Ottawa, Canada, August 26-30. find
  8. Guerberof, Ana. 2009. Productivity and quality in MT post-editing. In Beyond Translation Memories: New Tools for Translators Workshop, Ottawa, Canada, August 26-30. find
  9. Heinisch, Barbara and Vesna Lušicky. 2019. User expectations towards machine translation: A case study. In Forcada, Mikel, Andy Way, John Tinsley, Dimitar Shterionov, Celia Rico, and Federico Gaspari, editors, Proceedings of Machine Translation Summit XVII: Translator, Project and User Tracks, page… find
  10. Hieber, Felix, Michael Denkowski, Tobias Domhan, Barbara Darques Barros, Celina Dong Ye, Xing Niu, Cuong Hoang, Ke Tran, Benjamin Hsu, Maria Nadejde, Surafel Lakew, Prashant Mathur, Anna Currey, and Marcello Federico. 2022. Sockeye 3: Fast neural machine translation with pytorch. find
  11. Kallio, Hanna, Anna-Maija Pietilä, Martin Johnson, and Mari Kangasniemi. 2016. Systematic methodological review: Developing a framework for a qualitative semi-structured interview guide. Journal of advanced nursing, 72(12):2954–2965. find
  12. Knowles, Rebecca, Samuel Larkin, Marc Tessier, and Michel Simard. 2023. Terminology in neural machine translation: A case study of the Canadian Hansard. In Nurminen, Mary, Judith Brenner, Maarit Koponen, Sirkku Latomaa, Mikhail Mikhailov, Frederike Schierl, Tharindu Ranasinghe, Eva Vanmassenhove, Se… find
  13. Knowles, Rebecca, Samuel Larkin, Michel Simard, Marc A Tessier, Gabriel Bernier-Colborne, Cyril Goutte, and Chi-kiu Lo. 2024. Some tradeoffs in continual learning for parliamentary neural machine translation systems. In Knowles, Rebecca, Akiko Eriguchi, and Shivali Goel, editors, Proceedings of the… find
  14. Macken, Lieve, Daniel Prou, and Arda Tezcan. 2020. Quantifying the effect of machine translation in a high-quality human translation production process. Informatics, 7(2). find
  15. Moorkens, Joss and Sharon O’Brien. 2013. User attitudes to the post-editing interface. In O’Brien, Sharon, Michel Simard, and Lucia Specia, editors, Proceedings of the 2nd Workshop on Post-editing Technology and Practice, Nice, France, September 2. find
  16. Moorkens, Joss and Sharon O’Brien. 2015. Post-editing evaluations: Trade-offs between novice and professional participants. In El-Kahlout, İlknur Durgar, Mehmed Özkan, Felipe Sánchez-Martı́nez, Gema Ramı́rez-Sánchez, Fred Hollowood, and Andy Way, editors, Proceedings of the 18th Annual Conferenc… find
  17. Moorkens, Joss and Sharon O’Brien. 2017. Assessing user interface needs of post-editors of machine translation. In Kenny, Dorothy, editor, Human Issues in Translation Technology, pages 109–130. Routledge. find
  18. Naeem, Muhammad, Wilson Ozuem, Kerry Howell, and Silvia Ranfagni. 2023. A step-by-step process of thematic analysis to develop a conceptual model in qualitative research. International Journal of Qualitative Methods, 22:1–18. find
  19. “I don’t see it as a replacement, but rather as a helpful tool that provides clues and solutions. Sometimes, it suggests beautiful words I wouldn’t have thought of myself” (English translation, Interview Participant 8) find
  20. O’Brien, Sharon and Joss Moorkens. 2014. Towards intelligent post-editing interfaces. In Proceedings of the 20th FIT world congress, pages 131–137, August. find
  21. O’Brien, Audrey. 2002. Prism: The house of commons integrated technology project. Canadian Parliamentary Review, 25(2):20–22. find
  22. Quote 2: Sauro, Jeff and James R Lewis. 2016. Quantifying the user experience: Practical statistics for user research. Morgan Kaufmann. find
  23. “En raison de problèmes physiques, il n’est pas possible pour moi de taper quotidiennement le nombre élevé de mots que j’ai à traduire. C’est pour ces raisons que j’utilise maintenant Hawkeye” (French original quote, Questionnaire Participant 9) find
  24. Simard, Michel, Jeniffer Leal-Wyss, Gabriel Bernier-Colborne, and Rebecca Knowles. 2026. A longitudinal study of the adoption of specialized MT systems in Canadian parliamentary translation. In Proceedings of the 26th Annual Conference of the European Association for Machine Translation. find our card
  25. Stasimioti, Maria and Vilelmini Sosoni. 2020. Translation vs post-editing of NMT output: Insights from the English-Greek language pair. In Ortega, John E., Marcello Federico, Constantin Orasan, and Maja Popovic, editors, Proceedings of 1st Workshop on Post-Editing in Modern-Day Translation, pages 10… find
  26. Thompson, Edmund R. 2007. Development and validation of an internationally reliable short-form of the positive and negative affect schedule (PANAS). Journal of cross-cultural psychology, 38(2):227– 242. find
  27. Vieira, Lucas Nunes and Elisa Alonso. 2020. Translating perceptions and managing expectations: An analysis of management and production perspectives on machine translation. Perspectives, 28(2):163– 184. find
  28. Zaretskaya, Anna. 2015. The use of machine translation among professional translators. In Proceedings of the EXPERT Scientific and Technological Workshop, pages 1–12. find

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